DYNAMIC SOURCE LOCALIZATION AND FUNCTIONAL CONNECTIVITY ESTIMATION WITH STATE-SPACE MODELS: PRELIMINARY FEASIBILITY ANALYSIS

Dynamic imaging of source and functional connectivity (FC) using electroencephalographic (EEG) signals is essential for understanding the brain and cognition with sufficiently affordable technology to be widely applicable for studying changes associated with healthy ageing and the progression of neuropathology. We present an application for group analysis of recently developed state-space models and algorithms for simultaneously estimating the large-scale EEG inverse and FC problems. This approach reduces estimation bias and facilitates a detailed exploration and investigation of neuronal dynamics compared to current techniques. We present feasibility analyses for simulated and real EEG event-related data. The latter analysis uses a sixteen subjects EEG (Wakeman and Henson's) database, with signals recorded during a face-processing task. We implement a state-space methodology efficiently using an alternating least squares (ALS) algorithm. This application to neuroimaging analysis may be critical to reliably capture the brain dynamics despite interindividual variability, as demonstrated by the results presented.</p

RAWUL: A new ubiquitin-like domain in PRC1 Ring finger proteins that unveils putative plant and worm PRC1 orthologs

<p>Abstract</p> <p>Background</p> <p>Polycomb group (PcG) proteins are a set of chromatin-modifying proteins that play a key role in epigenetic gene regulation. The PcG proteins form large multiprotein complexes with different activities. The two best-characterized PcG complexes are the PcG repressive complex 1 (PRC1) and 2 (PRC2) that respectively possess histone 2A lysine 119 E3 ubiquitin ligase and histone 3 lysine 27 methyltransferase activities. While PRC2-like complexes are conserved throughout the eukaryotic kingdoms, PRC1-like complexes have only been described in Drosophila and vertebrates. Since both complexes are required for the gene silencing mechanism in Drosophila and vertebrates, how PRC1 function is realized in organisms that apparently lack PRC1 such as plants, is so far unknown. In vertebrates, PRC1 includes three proteins, Ring1B, Ring1A, and Bmi-1 that form an E3 ubiquitin ligase complex. These PRC1 proteins have an N-terminally located Ring finger domain associated to a poorly characterized conserved C-terminal region.</p> <p>Results</p> <p>We obtained statistically significant evidences of sequence similarity between the C-terminal region of the PRC1 Ring finger proteins and the ubiquitin (Ubq)-like family proteins, thus defining a new Ubq-like domain, the RAWUL domain. In addition, our analysis revealed the existence of plant and worm proteins that display the conserved combination of a Ring finger domain at the N-terminus and a RAWUL domain at the C-terminus.</p> <p>Conclusion</p> <p>Analysis of the conserved domain architecture among PRC1 Ring finger proteins revealed the existence of long sought PRC1 protein orthologs in these organisms, suggesting the functional conservation of PRC1 throughout higher eukaryotes.</p

Analyzing Social Construction of Knowledge Online by Employing Interaction Analysis, Learning Analytics, and Social Network Analysis

This article examines research methods for analyzing social construction of knowledge in online discussion forums. We begin with an examination of the Interaction Analysis Model (Gunawardena, Lowe, & Anderson, 1997) and its applicability to analyzing social construction of knowledge. Next, employing a dataset from an online discussion, we demonstrate how interaction analysis can be supplemented by employing other research techniques such as learning analytics and Social Network Analysis that shed light on the social dynamics that support knowledge construction. Learning analytics is the application of quantitative techniques for analyzing large volumes of distributed data ( big data ) in order to discover the factors that contribute to learning (Long & Siemens, 2011, p. 34). Social Network Analysis characterizes the information infrastructure that supports the construction of knowledge in social contexts (Scott, 2012). By combining interaction analysis with learning analytics and Social Network Analysis, we were able to conceptualize the process by which knowledge construction takes place in online platforms

Herbicide accumulation and evolution in reservoir sediments

The aim of the present study was to understand the effect of reservoir configurations on sediment pesticide fate. Two dams were selected on the River Garonne, in southwest France: Carbonne and Golfech, both with reservoirs subject to accumulation of herbicide-contaminated sediment. They are situated upstream and downstream respectively of an agricultural and urban area: the Mid-Garonne. The results presented include pesticide concentrations and C/N ratios in the smaller sediment particles (b2 mm) and values of oxygenation and herbicide concentrations in the water. The dynamic behaviour of sediment in the reservoirs is discussed. The present study shows that the theoretical lifespan (weak remanence in vitro) and the results actually observed in the sediment are conflicting. Pesticide contamination in Carbonne indicates conservation, even accumulation, of herbicide molecules while in Golfech transformation processes clearly dominate. The hydromorphological position of Golfech reservoir, i.e. located at the junction of two rivers with contrasting hydrological regimes and very different oxygenation conditions, leads to accelerated pesticide desorption or degradation. Unfortunately, this configuration is rare

Solving large-scale MEG/EEG source localisation and functional connectivity problems simultaneously using state-space models

State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high computational costs in large-scale analyses. Sparse inverse covariance estimators can mitigate these costs, but at the expense of a trade-off between enforced sparsity and increased estimation bias, necessitating careful assessment in low signal-to-noise ratio (SNR) situations. To address these challenges, we propose a three-fold solution: (1) Introducing multiple penalised state-space (MPSS) models that leverage data-driven regularisation; (2) Developing novel algorithms derived from backpropagation, gradient descent, and alternating least squares to solve MPSS models; (3) Presenting a K-fold cross-validation extension for evaluating regularisation parameters. We validate this MPSS regularisation framework through lower and more complex simulations under varying SNR conditions, including a large-scale synthetic magneto- and electro-encephalography (MEG/EEG) data analysis. In addition, we apply MPSS models to concurrently solve brain source localisation and functional connectivity problems for real event-related MEG/EEG data, encompassing thousands of sources on the cortical surface. The proposed methodology overcomes the limitations of existing approaches, such as constraints to small-scale and region-of-interest analyses. Thus, it may enable a more accurate and detailed exploration of cognitive brain functions

Activation of 1,2- and 1,3-Ketoamides with Thiourea Organocatalyst for the Enantioselective Domino Synthesis of Fuctionalized Cyclohexanes

International audienceSeveral reactive sites of 1,2- and 1,3-ketoamides were successively exploited in two complementary domino transformations for the synthesis of polysubstituted monocyclic or bridged bicyclic cyclohexanes, with the creation of up to six stereogenic centers. In both cases, a chiral bifunctional thiourea organocatalyst allowed efficient control of chirality in the final carbocycle

Deep Learning Models for Analyzing Social Construction of Knowledge Online

Gunawardena et al.’s (1997) Interaction Analysis Model (IAM) is one of the most frequently employed frameworks to guide the qualitative analysis of social construction of knowledge online. However, qualitative analysis is time consuming, and precludes immediate feedback to revise online courses while being delivered. To expedite analysis with a large dataset, this study explores how two neural network architectures—a feed-forward network (Doc2Vec) and a large language model transformer (BERT)—could automatically predict phases of knowledge construction using IAM. The methods interrogated the extent to which the artificial neural networks’ predictions of IAM Phases approximated a human coder’s qualitative analysis. Key results indicate an accuracy of 21.55% for Doc2Vec phases I-V, 43% for fine-tuning a pre-trained large language model (LLM), and 52.79% for prompt-engineering an LLM. Future studies for improving accuracy should consider either training the models with larger datasets or focusing on the design of prompts to improve classification accuracy. Grounded on social constructivism and IAM, this study has implications for designing and supporting online collaborative learning where the goal is social construction of knowledge. Moreover, it has teaching implications for guiding the design of AI tools that provide beneficial feedback for both students and course designers

Caching Using Software-Defined Networking in LTE Networks

Research ReportThe data consumption is increasing rapidly in mobile net- works. The cost of network infrastructure is increasing, which will lead to an ”end of profit” within next few years. Thus, mobile operators require technology that allows in- creasing the network capacity within low network costs. There- fore, using caching is the most evident solution to be used in their backhaul networks. However, the current architec- ture of LTE network does not provide sucient flexibility to place the caches in the most optimal locations. The current work on Software-Defined Networking in Evolved Packet Core virtualization has enabled us to integrate dy- namically the caching functionality in a LTE network and improve the caching system performance. In this paper, we present the solution we designed for this aim based on Soft- ware Defined Networking technology. Moreover, we developa testbed for the proof-of-concept and we present perfor- mance analysis of this solution

Pending recovery in the strength of the meridional overturning circulation at 26° N

The strength of the Atlantic meridional overturning circulation (AMOC) at 26∘ N has now been continuously measured by the RAPID array over the period April 2004–September 2018. This record provides unique insight into the variability of the large-scale ocean circulation, previously only measured by sporadic snapshots of basin-wide transport from hydrographic sections. The continuous measurements have unveiled striking variability on timescales of days to a decade, driven largely by wind forcing, contrasting with previous expectations about a slowly varying buoyancy-forced large-scale ocean circulation. However, these measurements were primarily observed during a warm state of the Atlantic multidecadal variability (AMV) which has been steadily declining since a peak in 2008–2010. In 2013–2015, a period of strong buoyancy forcing by the atmosphere drove intense water-mass transformation in the subpolar North Atlantic and provides a unique opportunity to investigate the response of the large-scale ocean circulation to buoyancy forcing. Modelling studies suggest that the AMOC in the subtropics responds to such events with an increase in overturning transport, after a lag of 3–9 years. At 45∘ N, observations suggest that the AMOC may already be increasing. Examining 26∘ N, we find that the AMOC is no longer weakening, though the recent transport is not above the long-term mean. Extending the record backwards in time at 26∘ N with ocean reanalysis from GloSea5, the transport fluctuations at 26∘ N are consistent with a 0- to 2-year lag from those at 45∘ N, albeit with lower magnitude. Given the short span of time and anticipated delays in the signal from the subpolar to subtropical gyres, it is not yet possible to determine whether the subtropical AMOC strength is recovering nor how the AMOC at 26∘ N responds to intense buoyancy forcing